Load Control Strategy Research Articles

Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques

Microgrids are an essential element of smart grids, which contain distributed renewable energy sources (RESs), energy storage devices, and load control strategies. Models built based on machine learning (ML) and deep learning (DL) offer hope for anticipating consumer demands and energy production from RESs. This study suggests an innovative approach for energy analysis based on the feature extraction and classification of microgrid photovoltaic cell data using deep learning algorithms. The energy optimization of a microgrid was carried out using a photovoltaic energy system with distributed power generation. The data analysis has been carried out for feature analysis and classification using a Gaussian radial Boltzmann with Markov encoder model. Based on microgrid energy optimization and data analysis, an experimental analysis of power analysis, energy efficiency, quality of service (QoS), accuracy, precision, and recall has been conducted. The proposed technique attained power analysis of 88%, energy efficiency of 95%, QoS of 77%, accuracy of 93%, precision of 85%, and recall of 77%.

Optimal guidance strategy for flexible load based on hybrid direct load control and time of use

The time-of-use (TOU) strategy can effectively improve the energy consumption mode of customers, reduce the peak-valley difference of load curve, and optimize the allocation of energy resources. This study presents an Optimal guidance mechanism of the flexible load based on strategies of direct load control and time-of-use. First, this study proposes a period partitioning model, which is based on a moving boundary technique with constraint factors, and the Dunn Validity Index (DVI) is used as the objective to solve the period partitioning. Second, a control strategy for the curtailable flexible load is investigated, and a TOU strategy is utilized for further modifying load curve. Third, a price demand response strategy for adjusting transferable load is proposed in this paper. Finally, through the case study analysis of typical daily flexible load curve, the efficiency and correctness of the proposed method and model are validated and proved.

Adaptive Power Line Communication for Low-Data-Rate Control and Sensing

Unreliable and range-limited communication restricts the effectiveness of many demand-side management schemes. For electrical loads with infrequent switching needs, untapped opportunities still exist to facilitate the reliable deployment of power line communication (PLC) for demand-side management applications. Inexpensive low-data-rate PLC, typically robust against power-line attenuation, can enable reliable load control in a building or facility. Yet, effective low-data-rate PLC schemes are susceptible to harmful and time-varying power-line noise levels. This paper presents approaches for adapting low-data-rate PLC to short-term and long-term noise variations often present in PLC channels. The proposed approaches enhance low-data-rate PLC performance, and further improve the reliability of load-control schemes that only require low data rates. Field demonstrations in a 24-floor apartment building with challenging conditions validate the proposed schemes.

Economic analysis based on saline water treatment using renewable energy system and microgrid architecture

Abstract Reverse osmosis desalination facilities operating on microgrids (MGs) powered by renewable energy are becoming more significant. A leader-follower structured optimization method underlies the suggested algorithm. The desalination plant is divided into components, each of which can be operated separately as needed. MGs are becoming an important part of smart grids, which incorporate distributed renewable energy sources (RESs), energy storage devices, and load control strategies. This research proposes novel techniques in economic saline water treatment based on MG architecture integrated with a renewable energy systems. This study offers an optimization framework to simultaneously optimize saline as well as freshwater water sources, decentralized renewable and conventional energy sources to operate water-energy systems economically and efficiently. The radial Boltzmann basis machine is used to analyse the salinity of water. Data on water salinity were used to conduct the experimental analysis, which was evaluated for accuracy, precision, recall, and specificity as well as computational cost and kappa coefficient. The proposed method achieved 88% accuracy, 65% precision, 59% recall, 65% specificity, 59% computational cost, and 51% kappa coefficient.

An experimental study of various load control strategies for an ammonia/hydrogen dual-fuel engine with the Miller cycle

Hydrogen and ammonia, as two renewable carbon-free combustibles, are considered to be potentially excellent alternative fuels for internal combustion engines. Since hydrogen and ammonia have opposite and complementary combustion characteristics, the co-combustion of ammonia with hydrogen is reasonable. However, there are few detailed experimental studies of load control strategies for an ammonia‑hydrogen engine. In this study, four load control strategies including the variation of ammonia volume share (AVS), qualitative control, quantitative control, and adjustment of intake variable valve timing (VVT) are proposed and assessed for their applicability in an ammonia/hydrogen dual-fuel engine. The ignition and combustion characteristics of ammonia do not allow for high thermal efficiency, so the Miller cycle is introduced to improve engine performance. The engine employs fuel supply modes of ammonia port injection and hydrogen direct injection at 1500 rpm. In general, the engine achieves higher brake mean effective pressure (BMEP) and brake thermal efficiency (BTE) at different AVS. Moreover, qualitative control and adjustment of intake VVT allow the engine to own a wide regulation range of BMEP and maintain the BTE of more than 37% under most conditions. However, quantitative control appears to be an inadequate strategy for the engine due to the lower BMEP and BTE.

Experimental study on the load control strategy of ammonia-hydrogen dual-fuel internal combustion engine for hybrid power system

The development of hybrid technology and the use of zero-carbon fuels are effective means to improve energy efficiency and reduce carbon emissions. In this study, the Miller cycle spark ignition engine was used, and four load control strategies of throttle, ammonia-hydrogen ratio, air–fuel ratio, and variable valve timing were used to conduct experimental research on the ammonia-hydrogen engine. The experimental results show that the throttle strategy provides the widest output range, and the BMEP is raised from a minimum of 1 bar to 7.4 bar. The ammonia-hydrogen mixing ratio strategy is suitable for cold start conditions, fully utilizing the good combustion characteristics of hydrogen, and at the same time reducing the amount of hydrogen used in the heat engine process as much as possible. The air–fuel ratio strategy provides the highest thermal efficiency with a maximum BTE approaching 40%. It satisfies the energy-saving demand under the single-point working condition of the engine in the series mode. The variable valve timing strategy can realize switching between BMEP between 3.5 bar and 6.5 bar, and keep BTE above 33%. The valve timing strategy can not only provide a wider load adjustment range but also maintain high thermal efficiency. Finally, combining the effects of the four control strategies and the typical operating modes of the hybrid system, this study suggests that the throttle strategy is suitable for the feed mode, the VVT strategy is suitable for the parallel mode, the ammonia-hydrogen mixture ratio strategy is suitable for the cold start condition, and the air–fuel ratio control is suitable for the series mode.

A novel dynamic load-priority-based scheduling strategy for home energy management system

In order to increase renewable energy consumption and enhance energy efficiency, residents participate in the utility in the form of demand response. To address the challenges of imprecise energy efficiency services and inadequate response of household energy use, a novel dynamic-load-priority-based scheduling strategy is proposed. Firstly, according to the operation law of household appliances, the control models of three kinds of flexible loads are established respectively. Secondly, according to the characteristics of household appliances, the priority evaluation index of household appliances including user comfort, electricity cost and task urgency index is proposed. The Technique for Order Preference by Similarity to Ideal Solution based on entropy weight is used to prioritize the household appliances that control the scheduling queue. Finally, according to the sorting results, the load control strategy is implemented to reasonably arrange the orderly work of household electrical equipment. Compared with the uncontrolled state, the proposed strategy can limit the total power of appliance load operation to the demand range in the peak load condition, and save 3% of electricity cost and reduce 5.4 kW of peak load under the demand limit of 12 kW, and 32% of electricity cost and 10.6 kW of peak load under the demand limit of 7 kW, without affecting the basic life of customers. In the period of new energy saturation, the load can also be dispatched to participate in the new energy consumption, thus realizing the coordinated interaction between residential energy consumption and the utility.

Research on AC Electronic Load with Energy Recovery Based on Finite Control Set Model Predictive Control

Nowadays, AC electronic loads with energy recovery are widely used in the testing of uninterruptible power supplies and power supply equipment. To tackle the problems of control difficulty, strategy complexity, and poor dynamic performance of AC electronic load with energy recovery of the conventional control strategy, a control strategy of AC electronic load with energy recovery based on Finite Control Set Model Predictive Control (FCS-MPC) is developed. To further reduce the computation burden of the FCS-MPC, a simplified FCS-MPC with transforming the predicted variables and using sector to select expected state is proposed. Through simplified model and equivalent approximation analysis, the transfer function of the system is obtained, and the stability and robustness of the system are analyzed. The performance of the simplified FCS-MPC is compared with space vector control (SVPWM) and conventional FCS-MPC. The results show that the FCS-MPC method performs better dynamic response and this advantage is more obvious when simulating high power loads. The simplified FCS-MPC shows similar control performance to conventional FCS-MPC at less computation burden. The control performance of the system also shows better simulation results.

Experimental assessment of a blended fatigue‐extreme controller employing trailing edge flaps

AbstractThis paper presents an experimental assessment of a blended fatigue‐extreme controller for load control employing trailing edge flaps on a lab‐scale wind turbine. The controller blends between a repetitive model predictive controller that targets fatigue loads and a dedicated extreme load controller, which consists of a simple on‐off load control strategy. The Fatigue controller uses the flapwise blade root bending moments of the three blades as input sensors. The Extreme controller additionally uses on‐blade angle of attack and velocity measurements as well as acceleration measurements to detect extreme events and to allow for a fast reaction. The experiments are conducted on the Berlin Research Turbine within the large wind tunnel of the TU Berlin. In order to reproduce test cases with deterministic extreme wind conditions that follow industry standards, the wind tunnel was redesigned. The analyzed test cases are extreme direction change, extreme coherent gust, extreme operating gust and extreme coherent gust with direction change. The test cases are analyzed by on‐blade angle of attack and velocity measurements. The load control performance of the Blended controller is compared to the pure fatigue oriented and the pure extreme load controller. The Blended controller achieves a maximum flapwise blade root bending moment reduction of 23%, which is comparable to the reduction achieved by the Extreme controller.

Can heat pump heat storage system perform better for space heating in China’s primary schools? A field test and simulation analysis

The space heating system has a significant influence on the energy consumption and comfortable indoor environment for primary schools in China’s cold area. This study investigated the operational performance of heat pump systems in two primary schools. Results showed that the primary school buildings had high heating load at turn-on period every morning, which then quickly decreased when the students came to classroom. With this wide-range variation of heating load and unreasonable control strategy, the mismatch between heat source and heating demand happened very easily among the whole heating season. It then led to typical issues and poor energy efficiency of heat pump systems, including excessive space heating after school, low load ratio and frequent start-stop operation of heat pump systems, small water temperature difference and excessive water flow rate of water distribution system, etc. In allusion to those core issues, the heat pump heat storage system (HPHS) was raised for space heating in primary schools to decouple the heat supply side and the heat demand side. Then this study simulated and analyzed the operational performance of HPHS. Results showed that with reasonable control strategy, the excessive heat supply after school was effectively avoided to save almost 51% of the total heat consumption. Besides, the heat pumps could operate stably with almost full heating load for heat storage, thus reaching better energy performance with annual average COP of 5.13. Also the water distribution systems in the ground side, heat storage side and user side could reach better energy performance with high energy efficiency and reasonable water temperature difference among the heating season. Therefore, the HPHSs with reasonable control strategy could effectively solve those typical issues and are more suitable for primary schools.

Design and application of new power load management system

In order to improve the load management ability of the new power system, this paper proposes a design scheme of the new power load management system. The design scheme is based on the cloud platform and adopts the micro service and micro application architecture to build applications such as load control customer management, equipment file management, load monitoring, load characteristic analysis, load control scheme formulation, load regulation and implementation, load implementation monitoring, implementation effect analysis, operation management, power safety, early warning monitoring, command and drill, and realize the real-time monitoring and accurate control of load resources on the customer side. The practical application proves the effectiveness of this scheme in improving the management of customer side load resources.

A Review of Flow Control for Gust Load Alleviation

Effective control of aerodynamic loads, such as maneuvering load and gust load, allows for reduced structural weight and therefore greater aerodynamic efficiency. After a basic introduction in the types of gusts and the current gust load control strategies for aircraft, we outline the conventional gust load alleviation techniques using trailing-edge flaps and spoilers. As these devices also function as high-lift devices or inflight speed brakes, they are often too heavy for high-frequency activations such as control surfaces. Non-conventional active control devices via fluidic actuators have attracted some attention recently from researchers to explore more effective gust load alleviation techniques against traditional flaps for future aircraft design. Research progress of flow control using fluidic actuators, including surface jet blowing and circulation control (CC) for gust load alleviation, is reviewed in detail here. Their load control capabilities in terms of lift force modulations are outlined and compared. Also reviewed are the flow control performances of these fluidic actuators under gust conditions. Experiments and numerical efforts indicated that both CC and surface jet blowing demonstrate fast response characteristics, capable for timely adaptive gust load controls.

Economic dispatch in a stand-alone system using a combinatorial energy management system

In this paper, a new adaptive combinatorial model control strategy is designed for energy dispatch of a photovoltaic-diesel-battery hybrid of an island grid power system, where the battery is optimised to increase its usage time, considering the increase of the fuel cost. The distinguishing feature of the proposed combinatorial step model is that the constraints of batteries are constructed depending on the load control strategy to describe the batteries' different modes (charging and discharging). Parameters of the battery vary with the load estimation and are estimated by the combinatorial process. In the combinatorial model, the control horizon varies according to the method defined by load control. The process steps of the control strategy are effectively used to obtain the dispatching behaviour for the renewable system while optimally considering various constraints of distributed power generation. Therefore, the system performance of the developed optimal combinatorial scheme is validated through simulation and analysis. The designed combinatorial model reduces the diesel generator (DG) power by more than 73 % of its total energy supply to the load.

Plug-Mate: An IoT-based occupancy-driven plug load management system in smart buildings

Plug load management systems are touted as promising solutions to reduce the rising energy consumption of plug loads in commercial buildings through different load monitoring and control strategies. However, their real-world applications remain relatively unexplored due to several issues related to deployment viability, energy-saving potentials, and system acceptance. Given these limitations, this paper proposes Plug-Mate, a novel IoT-based occupancy-driven plug load management system that reduces plug load energy consumption and user burden through intelligent plug load automation. The proposed system uses an interconnected network of modules and subsystems to perform plug load automation based on the users’ (1) high-resolution occupancy information obtained through a non-intrusive indoor localisation system, (2) plug load type information inferred through an advanced plug load identification feature, and (3) diverse control preferences through a personalised user interface. To demonstrate the system’s feasibility, six control strategies were evaluated during a 5-month field study in a university office space. Each control strategy involved different levels of plug load automation (i.e., manual, predefined schedules, and occupancy-driven) and was assessed based on their energy savings and user satisfaction levels to identify the optimal balance between automation and user control. Based on this evaluation, the best control strategy reported an average energy savings of 51.7% among different plug load types evaluated, achieving a 7.5% reduction in the building’s energy use and the highest user satisfaction score of 4.7 out of 5. Finally, we concluded this work by highlighting the system’s deployment feasibility for a building-wide implementation to guide future real-world applications. • A novel IoT-based occupancy-driven plug load management system is proposed. • Plug loads are automated based on occupancy, plug load type, and control preferences. • A 5-month field study was conducted to evaluate six plug load control strategies. • The system reported a 7.5% building-wide savings and user satisfaction score of 4.7/5. • The feasibility and economic viability of a building-wide deployment were discussed.

A novel approach for Direct Load Control of residential air conditioners for Demand Side Management in developing regions

The rapid growth of air conditioners (ACs) in developing regions exacerbates some of the issues present in their electricity systems. Direct Load Control strategies can manage peak loads for optimizing energy usage for users and utilities. When applied to air conditioning, these strategies can be used for Demand Side Management which has not been fully explored for developing regions. In this paper a novel Direct Load Control mechanism is proposed, wherein control is based on technical constraints (generation capacity). To determine the effects of the proposed mechanism, a case study of the Indian State of Karnataka is carried out. The results indicate 0.88% energy savings and a reduction of almost 2% in regional peak loads in a state where only around 5% of all households have AC units. The case study shows that an AC Direct Load Control can have significant economic and environmental impacts, and can help improve current energy, water and climate issues. Continuous improvements in both energy access and economic conditions of developing regions are leading to a larger number of AC's installed, meaning that a mechanism that enables sustainable AC consumption could be of great use for all the stakeholders in the developing countries' electricity sectors.

A Review of Air Conditioning Load Aggregation in Distribution Networks

In the context of global energy transformation, it is particularly important to tap the potential of flexible load on the distribution network side to participate in grid operation. As an important flexible adjustable load in the distribution network, air conditioning loads have typical characteristics of thermal energy storage, rapid response, and flexible scheduling, which is an ideal load resource. Effective and reliable load aggregation technologies for air conditioning load participation are the basis for operation in a power grid. To better understand the current research status of air conditioning load aggregation technologies and make full use of the existing research results to carry out further research, this paper comprehensively reviews the various aggregation technologies being used in distribution networks, including the modeling strategy for air conditioning load aggregation, control strategy, control method, and application analysis. Moreover, future research directions are summarized as a guide to improving the technology of air conditioning load aggregation.

Performance Estimation of a Downsized SI Engine Running with Hydrogen

Hydrogen is a carbon-free fuel that can be produced in many ways starting from different sources. Its use as a fuel in internal combustion engines could be a method of significantly reducing their environmental impact. In spark-ignition (SI) engines, lean hydrogen–air mixtures can be burnt. When a gaseous fuel like hydrogen is port-injected in an SI engine, working with lean mixtures, supercharging becomes very useful in order not to excessively penalize the engine performance. In this work, the performance of a turbocharged PFI spark-ignition engine fueled by hydrogen has been investigated by means of 1-D numerical simulations. The analysis focused on the engine behavior both at full and partial load considering low and medium engine speeds (1500 and 3000 rpm). Equivalence ratios higher than 0.35 have been considered in order to ensure acceptable cycle-to-cycle variations. The constraints that ensure the safety of engine components have also been respected. The results of the analysis provide a guideline able to set up the load control strategy of a SI hydrogen engine based on the variation of the air to fuel ratio, boost pressure, and throttle opening. Furthermore, performance and efficiency of the hydrogen engine have been compared to those of the base gasoline engine. At 1500 and 3000 rpm, except for very low loads, the hydrogen engine load can be regulated by properly combining the equivalence ratio and the boost pressure. At 3000 rpm, the gasoline engine maximum power is not reached but, for each engine load, lean burning allows the hydrogen engine achieving much higher efficiencies than those of the gasoline engine. At full load, the maximum power output decreases from 120 kW to about 97 kW, but the engine efficiency of the hydrogen engine is higher than that of the gasoline one for each full load operating point.

CVR Study and Active Power Loss Estimation Based on Analytical and ANN Method

Conservation through voltage reduction (CVR) aims to reduce the peak load and energy savings in electric power systems and is being deployed at various utilities. The effectiveness of the CVR program depends on the load characteristics, i.e., the sensitivity of the load to voltage variation, and voltage regulation device settings. In the current literature, there is a lack of discussion on the CVR factor calculation using different measurements, and there is a lack of method for active power loss estimation using substation measurements. This paper provides insights into CVR factor calculation based on the measurements captured at the substation and those at the load location. This paper also proposes a new method based on curve fitting and artificial neural network to estimate the active power loss using input active power, input reactive power and input voltage at the substation. The CVR comparison study conducted in this paper helps in understanding the factors affecting CVR factor and may provide guidance in CVR implementation and impact assessment. The proposed loss estimation method sheds light on the impacts of CVR in terms of load and loss reduction. The results based on simulation studies using the IEEE 13-bus and 34-bus systems are reported in this paper, noting that the proposed methods are applicable to larger systems, as long as the required measurements at the substation are available. Future research includes testing and refining the methods using large IEEE and utility distribution systems and considering the stochastic nature of the CVR factor with changing load and voltage regulator control schemes.

Calculation of dynamic assembly and tensioning loads at multiple points of prefabricated structure

Prefabricated underground station structure is rapidly assembled on site with modular components manufactured in factory. The longitudinal assembly accuracy of the components along the station’s longitudinal direction determines the construction quality of the station. In order to solve the problems encountered in the longitudinal pre-tensioning and assembly of structural components for a prefabricated station, such as low efficiency of manual operation, difficulty in determining the dynamic tensioning load, and insufficient accuracy of assembly control, a dynamic pre-tensioning load calculation method is proposed. The method, also referred to as centroid tracking pre-tensioning load algorithm, is based on controlling the dynamic relationship among the geometric centroid of rubber gasket, the centroid of multipoint tensioning load and the centroid of the elastic reaction force of rubber gasket. By using the dynamic correspondence between the compression reaction force distribution of the rubber gasket and the seam width under multipoint tensioning load, a loading control strategy is established to realize the balanced compression of the seam width of the prefabricated structure by controlling the load at each tensioning point. Thus, the centroid of the tensioning load continues to track the symmetry point of the centroid of the rubber gasket’s elastic reaction force with the geometric centroid of the rubber gasket as the reference. In this way, the tension value at each point can be precisely configured to improve the assembly accuracy of the components and realize seam width control. Based on the proposed algorithm, a set of tensioning and assembly equipment and control systems for prefabricated components of underground prefabricated structures are developed to carry out the automatic configuration of multipoint tensioning loads. The assembly tests show that the seam gap of assembled components is less than 1 mm. The proposed method demonstrates that the fast and high-precision automated assembly can be achieved and is conducive to improving the construction quality of prefabricated stations.

Control strategy of load following for ocean thermal energy conversion

Ocean thermal energy conversion (OTEC) provides a feasible solution for sustainable and stable power supply in remote islands. In this paper, a thermodynamic model of the OTEC system is developed to study the dynamic response of power output and superheating degree to manipulated variables. Afterward, a control strategy of load following for the island OTEC system is proposed. Finally, two controllers, MPC and PI, are designed and compared under fast and slow load changes as well as disturbance rejection test. The results indicate that the power output of OTEC system is sensitive to the speed of working-fluid pump, reaching the adjustment range of 14.7 kW when the nominal speed decreases by 30%. In addition, model predictive control (MPC) shows better performance in both rapid and slow load-change modes. Especially, in the rapid load-change mode, the MPC controller could always keep the evaporator outlet superheating in the range of 2–4.5 °C, while the PI controller may introduce no superheat that may cause liquid impact. Both MPC and PI controller can stabilize the power output at the set point under one day's measured disturbance, while MPC controller makes the superheating degree of evaporator outlet within smaller temperature fluctuations (<0.2 °C).